Bayesian statistics is a branch of statistics that relies on the principles of Bayesian probability theory to analyze data and make statistical inferences. In Bayesian statistics, prior knowledge or beliefs about the parameters of interest are combined with observed data to obtain a posterior distribution representing updated beliefs after seeing the data. This approach allows for the incorporation of uncertainty and variability in the data, and provides a flexible framework for modeling complex relationships and making predictions. Bayesian methods have applications in a wide range of fields such as machine learning, bioinformatics, economics, and psychology.